In many practical applications of simulation it is desirable to optimize the levels of integer or binary variables that are inputs for the simulation model. In these cases, the ob...
Seth D. Guikema, Rachel A. Davidson, Zehra Ç...
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Forecasting is of prime importance for accuracy in decision making. For data sets containing high autocorrelations, failure to account for temporal dependence will result in poor ...
In this paper, we present a two-level modeling approach to performance macromodeling based on radial basis function Support Vector Machine (SVM). The two-level model consists of a...
An m-sample semiparametric model in which the ratio of m - 1 probability density functions with respect to the mth is of a known parametric form without reference to any parametri...